Test Re-Test ICC Notebook

Here we import the relevant Packages to our project

library(ggsci)
library(cowplot)
library(ggthemes)
library(ggridges)
library(philentropy)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(psych)
library(ICC)
library(lattice)
library(lpSolve)
library(irr)
library(plot.matrix)
library(reshape)
library(plotly)
library(tidyverse)
library(svglite)

Now we need to Import the Data (Gradient 1, 2 and 3 of all the Subjects)

g1g2g3 = read.csv('G1G2G3AVG_results.csv')
g1g2g3$YeoNets = factor(g1g2g3$YeoNets, levels = c('VN','SMN','DAN','SN','LN','FPN','DMN'))
head(g1g2g3)

Data of Gradient 1

g1 = read.csv('G1_results.csv')
DMN_SMN_G1 = g1 %>% filter(YeoNets == 'DMN' | YeoNets == 'SMN' )
DMN_SMN_G1$YeoNets = factor(DMN_SMN_G1$YeoNets, levels = c('DMN','SMN'))

head(DMN_SMN_G1)

Plot of the Yeo Networks

data = g1g2g3 %>% filter(YeoNets != 'LN')
data$YeoNets = factor(data$YeoNets, levels = c('DMN','FPN','SN','DAN','SMN','VN'))

p2 <- ggplot(data, aes(x = Gradient1, y = YeoNets, color = YeoNets, fill = YeoNets)) +
  geom_density_ridges(alpha = 0.6, scale = 3) +
  scale_x_continuous(expand = c(0,0)) +
  scale_y_discrete(expand = c(0,0)) +
  scale_fill_manual(values = c('#CD3E4E','#E69422', '#BE3AFA', '#00760E', '#4682B4', '#781286')) +
  scale_color_manual(values = c('#CD3E4E','#E69422', '#BE3AFA', '#00760E', '#4682B4', '#781286')) +
  coord_cartesian(clip = 'off') +
  labs(y='', title='YeoNets') + theme_ridges() + 
  theme(legend.position = 'none',
        plot.title = element_text(face='bold', size=20, hjust=0.5))

p2

## Picking joint bandwidth of 0.00223

Now we plot for each sub only the DMN and SMN Networks

data = DMN_SMN_G1%>% select(-c(YeoNets,Yeo))
drops<- c("YeoNets","Yeo")
data= DMN_SMN_G1[,!(names(DMN_SMN_G1) %in% drops)]

p3<-ggplot(DMN_SMN_G1, aes(x= sub.1, color=YeoNets, fill = YeoNets)) +
  geom_density(alpha=0.4)+
  scale_x_continuous(limits = c(min(data%>%select(-X)),max(data%>%select(-X))))+
  scale_y_continuous(limits = c(0, 40))+
  labs(x="sub1 ses2",y='', title='YeoNets') + theme_ridges() + 
  theme(legend.position = 'none',
        plot.title = element_text(face='bold', size=10, hjust=0.5))

p3

Compute the ICC values of the DMN_SMN vector between each Subject

#split the data sessions 
ses1_df<- DMN_SMN_G1[,2:10]
ses2_df<- DMN_SMN_G1[,11:19]
iccMatrix<- matrix(ncol=ncol(ses1_df), nrow=ncol(ses2_df))



for(i in 1:ncol(ses1_df)){
  
  for(j in 1:ncol(ses2_df)){
  
    targetVec<- ses1_df[ ,i]
    otherVec<- ses2_df[,j]
    combinVec<-cbind(targetVec,otherVec)
    v<-icc(combinVec, model = "twoway", type = "agreement", unit = "single")$value
    iccMatrix[i,j]<-v
    
  }##end for j
  
  
}##end for i


matrix_df<- as.data.frame(iccMatrix)
colnames(matrix_df)<-c('Sub1','Sub2','Sub3','Sub4','Sub5','Sub6','Sub7','Sub8','Sub9')
rownames(matrix_df)<-c('Sub1','Sub2','Sub3','Sub4','Sub5','Sub6','Sub7','Sub8','Sub9')
y<- colnames(matrix_df)
x<- rownames(matrix_df)
y<-rep(y, times = 9)
dft<-t(combinVec)
df <- melt(matrix_df)

## Using  as id variables

pMatrix<-ggplot(data =df , aes(x=variable, y=y,fill= value)) + 
  geom_tile()+
  labs(x="ses1",y='ses2', title='ICC') +
  theme(plot.title = element_text(face='bold', size=10, hjust=0.5),axis.text.x = element_text(size=8, angle = 45, hjust = 1),axis.text.y = element_text(size=8,angle = 45, hjust = 1) )

pMatrix

ggsave(file="pmatrixSingle.png", pMatrix, width=6, height=5, dpi=400)
ggplotly(pMatrix)

ks_test value

DMN = DMN_SMN_G1 %>% filter(YeoNets == 'DMN')
SMN = DMN_SMN_G1 %>% filter(YeoNets == 'SMN')  
#Preper the data
DMN_ses1<- DMN[,2:10]
SMN_ses1<- SMN[,2:10]
DMN_ses2<- DMN[,11:19]
SMN_ses2<- SMN[,11:19]
ks_test_Matrix<- matrix(ncol=2, nrow=ncol(ses2_df))

head(DMN_ses1)

computing the ks_test value

for(i in 1:2){
  
  for(j in 1:ncol(ses2_df)){
    
    targetVec<- eval(parse(text = paste0("DMN_ses",i)))[ ,j]
    otherVec<- eval(parse(text = paste0("SMN_ses",i)))[ ,j]
    v<- ks.test(targetVec,otherVec)$stat
    ks_test_Matrix[j,i]<-v
    
  }##end for j
  
  
}##end for i

The results

ks_test_Matrix<-as.data.frame(ks_test_Matrix)
ks_test_Matrix$diff<-(ks_test_Matrix$V1-ks_test_Matrix$V2)
colnames(ks_test_Matrix)<-c("ses1","ses2","diff")
ks_test_Matrix

Compute th ICC value between the two ses Ks_test values

unit parameter = Average

icc(ks_test_Matrix[ , c("ses1","ses2")], model = "twoway", type = "agreement", unit = "average")

##  Average Score Intraclass Correlation
## 
##    Model: twoway 
##    Type : agreement 
## 
##    Subjects = 9 
##      Raters = 2 
##    ICC(A,2) = 0.822
## 
##  F-Test, H0: r0 = 0 ; H1: r0 > 0 
##   F(8,8.33) = 5.21 , p = 0.0141 
## 
##  95%-Confidence Interval for ICC Population Values:
##   0.186 < ICC < 0.96

unit parameter = Single

icc(ks_test_Matrix[ , c("ses1","ses2")], model = "twoway", type = "agreement", unit = "single")

##  Single Score Intraclass Correlation
## 
##    Model: twoway 
##    Type : agreement 
## 
##    Subjects = 9 
##      Raters = 2 
##    ICC(A,1) = 0.698
## 
##  F-Test, H0: r0 = 0 ; H1: r0 > 0 
##    F(8,8.3) = 5.21 , p = 0.0143 
## 
##  95%-Confidence Interval for ICC Population Values:
##   0.101 < ICC < 0.923